疫苗错误信息的基于方面的分类:使用Twitter聊天的时空分析
Heba Ismail1, Nada Hussein2, Rawan Elabyad3
1College of Engineering, Computer Science and Information Technology Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates. heba.ismail@adu.ac.ae.
BMC public health
|June 20, 2023
概括
社交媒体上关于COVID-19疫苗的错误信息显著影响公共卫生和疫苗接种. 这项研究开发了一个机器学习框架来分析和跟踪疫苗错误信息方面,揭示了在37%的研究国家对疫苗接种率的负面影响.
科学领域:
- 公共卫生 公共卫生
- 计算社会科学 计算社会科学
- 流行病学 流行病学
背景情况:
- 社交媒体上关于COVID-19疫苗接种的错误信息对公共安全构成重大威胁,并阻碍了全球恢复工作.
- 围绕疫苗的虚假叙述不鼓励接种,从而减缓恢复正常状态并影响社会福祉.
- 分析社交媒体内容以检测和理解疫苗错误信息对于开发有效的对策和支持知情决策至关重要.
研究的目的:
- 开发和验证一个框架来分析社交媒体上的疫苗错误信息方面的时空进展.
- 识别和量化疫苗错误信息中的常见主题,以帮助利益相关者做出决策.
- 提供有关各种COVID-19疫苗的错误信息传播的最新见解.
主要方法:
- 将大约3800条推特注释为四个经过专家验证的疫苗错误信息方面.
- 基于方面的错误信息分析框架的开发,使用光梯度增强机 (LightGBM) 模型.
- 错误信息进展的时空空间统计分析和43个国家的错误信息数量与疫苗接种率之间的皮尔森相关系数的计算.
主要成果:
- 轻GBM模型实现了高分类准确性,ROC曲线下的面积 (AUC) 在验证中为90.3%,在测试中为89.6%.
- 具体方面准确度包括87.4%的"疫苗成分",92.7%的"不良反应",80.1%的"议程",82.5%的"有效性和临床试验".
- 相关性分析表明,37%的研究国家在2020年12月至2021年7月期间因Twitter错误信息而减少了疫苗接种.
结论:
- 推特是了解疫苗错误信息的动态的一个有价值的平台.
- 像LightGBM这样的机器学习模型对于错误信息的多类分类是有效的,即使在有限的社交媒体数据中也显示出可靠性.
- 这些发现强调了在线错误信息对公共卫生倡议,特别是疫苗部署的切实负面影响.
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